Repeated measures random forests (RMRF): Identifying factors associated with nocturnal hypoglycemia

Nocturnal hypoglycemia is a common phenomenon among patients with diabetes and can lead to a broad range of adverse events and complications. Identifying factors associated with hypoglycemia can improve glucose control and patient care. We propose a repeated measures random forest (RMRF) algorithm that can handle nonlinear relationships and interactions and the correlated responses from patients evaluated over several nights. Simulation results show that our proposed algorithm captures the informative variable more often than naïvely assuming independence. RMRF also outperforms standard random forest and extremely randomized trees algorithms. We demonstrate scenarios where RMRF attains greater prediction accuracy than generalized linear models. We apply the RMRF algorithm to analyze a diabetes study with 2524 nights from 127 patients with type 1 diabetes. We find that nocturnal hypoglycemia is associated with HbA1c, bedtime blood glucose (BG), insulin on board, time system activated, exercise intensity, and daytime hypoglycemia. The RMRF can accurately classify nights at high risk of nocturnal hypoglycemia.     

Embodied Resource Flows in a Global Economy

This article presents a methodology for identifying critical links in global resource supply chains by tracking resources from their extraction in one region of the world economy through their embodiment in intermediate products in the same and other regions to eventual embodiment in final goods. We build on previous work that applied an absorbing Markov chain (AMC) to results obtained using an input‐output (IO) model of a single region to define a resource‐specific network within that economy. In the absence of model calculations, the AMC can also be applied to standard IO data for a past year. This article first generalizes the analytic framework from a single region to the important case of the global resource‐specific network. This network typically includes cycling of embodied resources between sectors not only within each economy, but also among regions, as subsequent rounds of intermediate products are traded. Next, we refine that analysis to exhibit a crucial subnetwork, the resource end‐use network, which only tracks the portion of the resource that ends up embodied in a specific final product in a given region. Finally, we develop techniques to distinguish key branches of these networks and provide detailed insights about the structure of global resource dependence. A numerical example is applied to results of scenario analysis using an IO model of the world economy. Two alternative scenarios are compared. In each scenario, embodied resources are carried over specific branches of a global network in three regions using three resources to produce four goods.     

Is Maternal Marriage Beneficial for Low-Income Adolescents?

The present study investigated the association of mothers' marriage and changes in young adolescents' cognitive and socioemotional development and changes in family processes. Analyses employed longitudinal data from the Three-City Study to track maternal partnerships for 860 low-income adolescents (10–14 years-old in Wave 1) across a 16 month period. No short-term benefits or risks emerged for youth when mothers entered marriage, with few changes in family or maternal functioning linked with marriage formation as well. In contrast, adolescents in stably married families experienced improved academic, behavioral, and psychological well-being compared to youth in stable cohabiting or single-parent families. Stable marriage was similarly linked to improvements across multiple domains of home and mothers' functioning. These patterns were not moderated by the male partner's identity (biological father or stepfather). Results support the benefits of stable marriage on youth development, but suggest that policies supporting movements into new marriages may not result in improved adolescent or family functioning, at least in the short term.     

The utility of epithelial-cell micronuclei in the assessment of intermittent exposures

Epithelial-cell micronuclei (MN) are potentially useful markers of occupational exposure to genotoxicants. With intermittent exposures, cells sampled either before or after a specific time interval, reflecting the time it takes for damaged cells to become available at the epithelial surface, are unlikely to be exposure-related. It may then be important to conduct an exposure-window analysis, with the goal of identifying the relevant exposures.We re-analysed individual exposure data from a previous study (Suruda et al. 1993) of MN formation in 22 male mortuary science students exposed to formaldehyde during a 90-day embalming class. We conducted an exposurewindow analysis and compared the results with those obtained with 90-day cumulative exposure. The window widths varied between 7 and 25 days, in 1 day increments, assuming a constant 7-day cell-cycle. We assessed the fit (likelihood-ratio test) of a linear regression model, regressing the change in buccal MN prevalence on formaldehyde exposure, using both asymptotic and non-asymptotic methods. Exposures defined from 7-15 to 7-18 days before specimen collection provided a slightly better fit than the 90-day cumulative exposure, with a doubling of the regression coefficient for the exposure effect (for the 7-16-days window LR = 5.32, p = 0.032, coefficient = 0.088 MN per 1000 cells per ppm-hr; 95% CI = 0.014, 0.16; for the 90-day cumulative exposure LR = 4.44, p = 0.048, coefficient = 0.045 MN per 1000 cells per ppm-hr, 95% CI = 0.0038, 0.086). Although hampered by the small number of subjects, these results reinforce the potential importance of exposure timing.     

Prediction of Severe Disease in Children with Diarrhea in a Resource-Limited Setting

Objective

To investigate the accuracy of three clinical scales for predicting severe disease (severe dehydration or death) in children with diarrhea in a resource-limited setting.

Methods

Participants included 178 children admitted to three Rwandan hospitals with diarrhea. A local physician or nurse assessed each child on arrival using the World Health Organization (WHO) severe dehydration scale and the Centers for Disease Control (CDC) scale. Children were weighed on arrival and daily until they achieved a stable weight, with a 10% increase between admission weight and stable weight considered severe dehydration. The Clinical Dehydration Scale was then constructed post-hoc using the data collected for the other two scales. Receiver Operator Characteristic (ROC) curves were constructed for each scale compared to the composite outcome of severe dehydration or death.

Results

The WHO severe dehydration scale, CDC scale, and Clinical Dehydration Scale had areas under the ROC curves (AUCs) of 0.72 (95% CI 0.60, 0.85), 0.73 (95% CI 0.62, 0.84), and 0.80 (95% CI 0.71, 0.89), respectively, in the full cohort. Only the Clinical Dehydration Scale was a significant predictor of severe disease when used in infants, with an AUC of 0.77 (95% CI 0.61, 0.93), and when used by nurses, with an AUC of 0.78 (95% CI 0.63, 0.93).

Conclusions

While all three scales were moderate predictors of severe disease in children with diarrhea, scale accuracy varied based on provider training and age of the child. Future research should focus on developing or validating clinical tools that can be used accurately by nurses and other less-skilled providers to assess all children with diarrhea in resource-limited settings.